In this article we will see how we can apply 2D laplacian filter to the image in mahotas. A Laplacian filter is an edge detector used to compute the second derivatives of an image, measuring the rate at which the first derivatives change. This determines if a change in adjacent pixel values is from an edge or continuous progression.
In this tutorial we will use “lena” image, below is the command to load it.
mahotas.demos.load('lena')
Below is the lena image
In order to do this we will use mahotas.laplacian_2D method
Syntax : mahotas.laplacian_2D(img)
Argument : It takes image object as argument
Return : It returns image object
Note : Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this
image = image[:, :, 0]
Below is the implementation
Python3
# importing required librariesimport mahotasimport mahotas.demosfrom pylab import gray, imshow, showimport numpy as npimport matplotlib.pyplot as plt # loading imageimg = mahotas.demos.load('lena') # filtering imageimg = img.max(2)print("Image") # showing imageimshow(img)show()# applying 2D Laplacian filternew_img = mahotas.laplacian_2D(img) # showing imageprint("2D Laplacian filter")imshow(new_img)show() |
Output :
Image
2D Laplacian filter
Another example
Python3
# importing required librariesimport mahotasimport numpy as npfrom pylab import gray, imshow, showimport osimport matplotlib.pyplot as plt # loading imageimg = mahotas.imread('dog_image.png')# filtering imageimg = img[:, :, 0] print("Image") # showing imageimshow(img)show()# applying 2D Laplacian filternew_img = mahotas.laplacian_2D(img) # showing imageprint("2D Laplacian filter")imshow(new_img)show() |
Output :
Image
2D Laplacian filter
